Machine learning methods comparison by using statistical tests in solar energy forecasting based on weather features

Climate change necessitates precise solar forecasting due to its weather-dependent intermittency. Key parameters - temperature, visibility, altitude, pressure, and wind speed - were analyzed using non-parametric tests. We prioritized short-term weather patterns over random data splitting for enhanced accuracy.Non-parametric tests, such as the Kolmogorov-Smirnov test, were used to assess data normality and select highly correlated features. Principal Component Analysis (PCA) reduces dataset dimensionality while preserving critical trends. Various machine learning approaches were evaluated, including: weighted linear regression (both with and without dimensionality reduction), boosted regression trees, and deep learning architectures-comprising both fundamental models (Convolutional Neural Networks [CNNs] and Recurrent Neural Networks [RNNs]) and advanced hybrid architectures (Temporal Convolutional Networks (TCN) Convolutional Neural Network-Long Short-Term Memory network (CNN-LSTM). All models were optimized through systematic hyperparameter tuning to enhance predictive performance, reduce computational complexity, and improve learning convergence rates. Special attention was given to addressing vanishing gradient problems in deep neural network implementations. Results show TCN outperform other deep learning models, achieving lower training and testing errors with fewer parameters and reduced time complexity. CNN-LSTM models, designed for spatial-sequence prediction, perform well but require more parameters and computational time. The lowest test and training errors belong to CNN-LSTM and TCN, with approximately 9 % and 2 % lower than the maximum amount, respectively. A trade-off between model complexity, error rates, and computational efficiency must be considered when selecting the optimal approach. Since relevant weather features vary by location, the proposed methodology serves as an adaptable algorithm for solar energy prediction in diverse geographical regions.